To test the generalization capability of U-S-VAE, we provide its performance on different datasets, such as the NTU RGB+D 60 [1]}, which is a large scale dataset commonly used for testing action/gesture models. NTU RGB+D contains 60 action categories collected from 40 subjects. In video capturing, each action is recorded simultaneously by three cameras at different horizontal angles. As such, not merely provided the commonly cross-subject (C-Sub) protocol, the authors of NTU RGB+D also recommended the cross-view (C-View) evaluation. We follow this convention and report the recognition accuracy (Top-1) of the two protocols. Here, the results of RGB-D-based unsupervised methods are presented, including Shuffle & Learn [2]}, and models of Luo et al. [3]} and Li et al. [4]}. These models rely on the depth information which are not available in iMiGUE so that we cannot evaluate their performance on our dataset. Moreover, two pose-based models, the LongT GAN [5]} and P&C [6]} are compared for evaluation analysis. Because the code of LongT GAN is not released, we cannot report its result on iMiGUE. Finally, for ablation studies on encoder with different number of BLSTM layer, U-S-VAE with 2-layers BLSTM (U-S-VAE 2L) and 3-layers (U-S-VAE 3L) are chosen for comparison. Ablation studies on different features for classification are carried, and we select the cell states of BLSTM to serve as the feature vectors for testing (U-S-VAE 1L w C). In Table REF , we report these experimental results.
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